System and method for analysis of graph databases using intelligent reasoning systems

ABSTRACT

A system for analyzing graph databases using intelligent reasoning systems including scalable collection of, and transformation of, graph data into facts suitable for use with programming logic languages doing deductive reasoning. A graph analyzer ingests disparate graph data from across the Internet and transforms the graph data into a fact table. In order to reduce latency and processing congestion, a stream processing engine and sharding strategy are employed to ensure scalability through parallelized processing of programming logic queries. Transformed graph data, now relational data, is utilized with programming logic languages that allow for hypothetical queries whereby an inference engine can deduce new information to satisfy such a query. Furthermore, the self-contained nature of inputs, outputs, and transformations of the system means strict data provenance can be observed and adhered to.

CROSS-REFERENCE TO RELATED APPLICATIONS

application No. Date Filed Title Current Herewith SYSTEM AND METHOD FORANALYSIS application OF GRAPH DATABASES USING INTELLIGENT REASONINGSYSTEMS Is a continuation-in-part of: 16/864,133 Apr. 30, 2020MULTI-TENANT KNOWLEDGE GRAPH DATABASES WITH DYNAMIC SPECIFICATION ANDENFORCEMENT OF ONTOLOGICAL DATA MODELS which is a continuation-in-partof: 15/847,443 Dec. 19, 2017 SYSTEM AND METHOD FOR AUTOMATIC CREATION OFONTOLOGICAL DATABASES AND SEMANTIC SEARCHING which is acontinuation-in-part of: 15/790,457 Oct. 23, 2017 DISTRIBUTABLE MODELWITH BIASES CONTAINED WITHIN DISTRIBUTED DATA which claims benefit of,and priority to: 62/568,298 Oct. 4, 2017 DISTRIBUTABLE MODEL WITH BIASESCONTAINED IN DISTRIBUTED DATA and is also a continuation-in-part of:15/790,327 Oct. 23, 2017 DISTRIBUTABLE MODEL WITH DISTRIBUTED DATA whichclaims benefit of, and priority to: 62/568,291 Oct. 4, 2017DISTRIBUTABLE MODEL WITH DISTRIBUTED DATA and is also acontinuation-in-part of: 15/616,427 Jun. 7, 2017 RAPID PREDICTIVEANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR-DRIVEN DISTRIBUTEDCOMPUTATIONAL GRAPH and is also a continuation-in-part of: 15/141,752Apr. 28, 2016 SYSTEM FOR FULLY INTEGRATED CAPTURE, AND ANALYSIS OFBUSINESS INFORMATION RESULTING IN PREDICTIVE DECISION MAKING ANDSIMULATION which is a continuation-in-part of: 15/091,563 Apr. 5,2016SYSTEM FOR CAPTURE, ANALYSIS AND U.S. Pat. No. Issue Date STORAGE OFTIME SERIES DATA FROM 10,204,147 Feb. 12, 2019 SENSORS WITHHETEROGENEOUS REPORT INTERVAL PROFILES and is also acontinuation-in-part of: 14/986,536 Dec. 31, 2015 DISTRIBUTED SYSTEM FORLARGE U.S. Pat. No. Issue Date VOLUME DEEP WEB DATA 10,210,255 Feb. 19,2019 EXTRACTION and is also a continuation-in-part of: 14/925,974 Oct.28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THEDISTRIBUTED COMPUTATIONAL GRAPH Current Herewith SYSTEM AND METHOD FORANALYSIS application OF GRAPH DATABASES USING INTELLIGENT REASONINGSYSTEMS Is a continuation-in-part of: 16/864,133 Apr. 30, 2020MULTI-TENANT KNOWLEDGE GRAPH DATABASES WITH DYNAMIC SPECIFICATION ANDENFORCEMENT OF ONTOLOGICAL DATA MODELS which is a continuation-in-partof: 15/847,443 Dec. 19, 2017 SYSTEM AND METHOD FOR AUTOMATIC CREATION OFONTOLOGICAL DATABASES AND SEMANTIC SEARCHING which is acontinuation-in-part of: 15/616,427 Jun. 7, 2017 RAPID PREDICTIVEANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR-DRIVEN DISTRIBUTEDCOMPUTATIONAL GRAPH which is a continuation-in-part of: 14/925,974 Oct.28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THEDISTRIBUTED COMPUTATIONAL GRAPH Current Herewith SYSTEM AND METHOD FORANALYSIS application OF GRAPH DATABASES USING INTELLIGENT REASONINGSYSTEMS Is a continuation-in-part of: 16/864,133 Apr. 30, 2020MULTI-TENANT KNOWLEDGE GRAPH DATABASES WITH DYNAMIC SPECIFICATION ANDENFORCEMENT OF ONTOLOGICAL DATA MODELS which is a continuation-in-partof: 15/847,443 Dec. 19, 2017 SYSTEM AND METHOD FOR AUTOMATIC CREATION OFONTOLOGICAL DATABASES AND SEMANTIC SEARCHING which is acontinuation-in-part of: 15/489,716 Apr. 17, 2017 REGULATION BASEDSWITCHING SYSTEM FOR ELECTRONIC MESSAGE ROUTING which is acontinuation-in-part of: 15/409,510 Jan. 18, 2017 MULTI-CORPORATIONVENTURE PLAN VALIDATION EMPLOYING AN ADVANCED DECISION PLATFORM which isa continuation-in-part of: 15/379,899 Dec. 15, 2016 INCLUSION OF TIMESERIES GEOSPATIAL MARKERS IN ANALYSES EMPLOYING AN ADVANCED CYBER-DECISION PLATFORM which is a continuation-in-part of: 15/376,657 Dec.13, 2016 QUANTIFICATION FOR INVESTMENT U.S. Pat. No. Issue Date VEHICLEMANAGEMENT EMPLOYING 10,402,906 Sep. 3, 2019 AN ADVANCED DECISIONPLATFORM which is a continuation-in-part of: 15/237,625 Aug. 15, 2016DETECTION MITIGATION AND U.S. Pat. No. Issue Date REMEDIATION OFCYBERATTACKS 10,248,910 Apr. 2, 2019 EMPLOYING AN ADVANCED CYBER-DECISION PLATFORM which is a continuation-in-part of: 15/206,195 Jul. 8,2016 ACCURATE AND DETAILED MODELING OF SYSTEMS WITH LARGE COMPLEXDATASETS USING A DISTRIBUTED SIMULATION ENGINE which is acontinuation-in-part of: 15/186,453 Jun. 18, 2016 SYSTEM FOR AUTOMATEDCAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR RELIABLE BUSINESSVENTURE OUTCOME PREDICTION which is a continuation-in-part of:15/166,158 May 26, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OFBUSINESS INFORMATION FOR SECURITY AND CLIENT-FACING INFRASTRUCTURERELIABILITY which is a continuation-in-part of: 15/141,752 Apr. 28, 2016SYSTEM FOR FULLY INTEGRATED CAPTURE, AND ANALYSIS OF BUSINESSINFORMATION RESULTING IN PREDICTIVE DECISION MAKING AND SIMULATION theentire specification of each of which is incorporated herein byreference.

BACKGROUND Field of the Art

The disclosure relates to the field of data analytics, and moreparticularly to the field of data engineering.

Discussion of the State of the Art

Over the past forty years the relational database (i.e., fact table)model has been widely used for data storage applications. In more recentyears, as data sets grow ever larger, there has been a shift to use theincreasingly popular graph database model. Unlike relational databases,which store data in tables, graph databases use nodes and relationshipsbetween nodes to store data and are significantly more efficient forhandling large data sets for certain query patterns as relationshipsbetween entities are explicitly encoded as opposed to inferred fromjoins as in many other popular data models. This poses challenges withrespect to the push for a semantic web, where the need for deducingrelationships between geographically disperse data is increasinglyparamount.

Over the years, fact tables have found uses as deductive databases forprogramming logic languages which are capable of deducing new knowledgeby employing rules based on the facts present in relational tables. Andwhile graph databases are especially powerful over relational/tabledatabases for existing relationships and information, they currentlylack comprehensive support for programming logic languages that wouldallow deduction of new information. This is a significant problem infields such as semantic analysis as two competing and incompatible datastorage models impede the work of data scientists and data users alike.If programming logic languages are to exploit the considerable amount ofgraph data for deductive reasoning, there must exist a system and methodfor transforming and using graph data into a standard schema.

What is needed is a system and method for analyzing graph databasesusing intelligent reasoning systems including scalable collection of,and transformation of, graph data into facts suitable for use withprogramming logic languages for deductive reasoning.

SUMMARY

Accordingly, the inventor has conceived and reduced to practice, asystem and method for analyzing graph databases using intelligentreasoning systems including scalable collection of, and transformationof, graph data into facts suitable for use with programming logiclanguages doing deductive reasoning. A graph analyzer ingests disparategraph data from across the Internet and transforms the graph data into afact table. In order to reduce latency and processing congestion, astream processing engine and sharding strategy are employed to ensurescalability through parallelized processing of programming logicqueries. Transformed graph data, now relational data, is utilized withprogramming logic languages that allow for hypothetical queries wherebyan inference engine can deduce new information to satisfy such a query.Furthermore, the self-contained nature of inputs, outputs, andtransformations of the system means strict data provenance can beobserved and adhered to.

According to a preferred embodiment, a system analyzing graph databasesusing intelligent reasoning systems including scalable collection of,and transformation of, graph data into facts suitable for use withprogramming logic languages for deductive reasoning is disclosed,comprising: a computing device comprising a memory, a processor, and anon-volatile data storage device; a stream processing engine comprisinga first plurality of programming instructions stored in the memory of,and operating on the processor of, the computing device, wherein thefirst plurality of programming instructions, when operating on theprocessor, cause the computing device to: receive an ontology-mediatedquery, wherein the ontology-mediated query is constructed by aprogramming logic language; ingest one or more graph-based databasesrelated to the ontology-mediated query; identify a storage technologyand data model of each graph-based database; and send the identifiedstorage technology and data model of each graph-based database to atranslation service; a translation service comprising a second pluralityof programming instructions stored in the memory of, and operating onthe processor of, the computing device, wherein the second plurality ofprogramming instructions, when operating on the processor, cause thecomputing device to: convert each graph-based database to facts suitablefor use with programming logic languages for deductive reasoning,wherein the conversion is based on the identified storage technology anddata model; and send the facts suitable for use with programming logiclanguages for deductive reasoning to a sharding service; and a shardingservice comprising a second plurality of programming instructions storedin the memory of, and operating on the processor of, the computingdevice, wherein the second plurality of programming instructions, whenoperating on the processor, cause the computing device to: combine thefacts suitable for use with programming logic languages for deductivereasoning from the graph-based databases into a fact table; and asemantic reasoner comprising a third plurality of programminginstructions stored in the memory of, and operating on the processor of,the computing device, wherein the third plurality of programminginstructions, when operating on the processor, cause the computingdevice to: satisfy the ontology-mediated query by analyzing the facttable; and output the ontology-mediated query results.

According to a preferred embodiment, a method analyzing graph databasesusing intelligent reasoning systems including scalable collection of,and transformation of, graph data into facts suitable for use withprogramming logic languages for deductive reasoning is disclosed,comprising the steps of: receiving an ontology-mediated query, whereinthe ontology-mediated query is constructed by a programming logiclanguage; ingesting one or more graph-based databases related to theontology-mediated query; identifying a storage technology and data modelof each graph-based database; converting each graph-based database tofacts suitable for use with programming logic languages for deductivereasoning, wherein the conversion is based on the identified storagetechnology and data model; combining the facts suitable for use withprogramming logic languages for deductive reasoning from the graph-baseddatabases into a fact table; and providing the fact table to theprogramming logic language to satisfy the ontology-mediated query;satisfying the ontology-mediated query by analyzing the fact table; andoutputting the ontology-mediated query results.

According to one aspect, the system further comprises an event log,wherein the event log provides a means for data provenance.

According to one aspect, the stream processing engine uses distributedcomputing to process the ontology-mediated query.

According to one aspect, the system further comprises a semanticreasoner.

According to one aspect, the semantic reasoner deduces new informationfrom the fact table.

According to one aspect, the new information is integrated into the facttable.

According to one aspect, the semantic reasoner uses the fact table tosatisfy a hypothetical query.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a block diagram illustrating an exemplary system architecturefor a large-scale graph analyzer.

FIG. 2 is a block diagram of an exemplary method for translating nativeproperty graph data into translational data.

FIG. 3 is a flow diagram illustrating an exemplary method for alarge-scale graph analyzer, according to one embodiment.

FIG. 4 is a flow diagram illustrating an exemplary method for graph dataconversion, according to one aspect.

FIG. 5 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 6 is a block diagram illustrating an exemplary logical architecturefor a client device.

FIG. 7 is a block diagram showing an exemplary architectural arrangementof clients, servers, and external services.

FIG. 8 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

DETAILED DESCRIPTION

The inventor has conceived and reduced to practice a system and methodfor analyzing graph databases using intelligent reasoning systemsincluding scalable collection of, and transformation of, graph data intofacts for use with programming logic languages for deductive reasoning.A graph analyzer ingests disparate graph data from across the Internetand transforms the graph data into a fact table, i.e., facts suitablefor use with programming logic languages for deductive reasoning. Inorder to reduce latency and processing congestion, a stream processingengine and sharding strategy are employed to ensure scalability throughparallelized processing of programming logic queries. Transformed graphdata, now relational data, is utilized with programming logic languagesthat allow for hypothetical queries whereby an inference engine candeduce new information to satisfy such a query. Furthermore, theself-contained nature of inputs, outputs, and transformations of thesystem means strict data provenance can be observed and adhered to.

Currently, there exist various formats of, and standards for, graphdata. There exist limited solutions for one-off transformations of graphdata to relational data (facts suitable for use with programming logiclanguages for deductive reasoning) but on small and targeted data setswith little to no practical value. This disclosure describes acentralized system that comprises a comprehensive transformationsolution whereby all graph data can be utilized by programming logiclanguages for deductive queries.

An ontology-mediated query, typically specified by Web Ontology Language(OWL) or Resource Description Framework (RDF), is posed to a programminglogic language such as Datalog, Prolog, or other logic programminglanguage. The query is handled by the stream processing engine (e.g.,Flink as one example) which can process the query as an event-driven andscalable bounded or unbounded stream. The stream processing enginehandles the jobs and tasks required to retrieve the relevant informationfrom various graph data sources working in conjunction with agraph-processing API (e.g. Gelly).

As relevant nodes and relationships are ingested from around theInternet, the translation service identifies the storage structure anddata model which it uses to apply the correct transformation to eachnode and relation into a separate fact, which is then stored in as factssuitable for use with programming logic languages for deductivereasoning (i.e., a relational table). A sharding service handles thedata persistence with respect to the fact tables where significantlylarge tables are horizontally partitioned across a cluster of databasenodes. Once the fact tables have been populated, the semantic reasoneruses the logic language and the ontology language to specify a set ofinference rules where the query is then processed and satisfied. If thequery requires data, an inference engine can produce such data usingdeductive logic. Finally, since the inputs, outputs, and transformationsare captured wholly within the system, a means of data provenance isprovided which also lends itself to schema enforcement.

An ontology-mediated query, typically specified by web ontology language(OWL) or resource description framework (RDF), is posed to a programminglogic language such as Datalog, Prolog, or other logic programminglanguage. The query is handled by the stream processing engine (e.g.,Mink) which can process the query as an event-driven and scalablebounded or unbounded stream. The stream processing engine handles thejobs and tasks required to retrieve the relevant information fromvarious graph data sources working in conjunction with agraph-processing API (e.g. Gelly). As relevant nodes and relationshipsare ingested from around the Internet, the translation service convertseach node and relation into a separate fact, which is then stored in asfacts suitable for use with programming logic languages for deductivereasoning (i.e., a relational table). A sharding service handles thedata persistence layer with respect to the fact tables wheresignificantly large tables are horizontally partitioned across a clusterof database nodes. Once the fact tables have been populated, thesemantic reasoner uses the logic language and the ontology language tospecify a set of inference rules where the query is then processed andsatisfied. Since the inputs, outputs, and transformations are capturedwithin the system, this also provides a means of data provenance whichfurther enforces schema trustworthiness and enforcement.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Conceptual Architecture

FIG. 1 is a block diagram illustrating an exemplary system architecture100 for a large-scale graph analyzer. According to one embodiment, alarge-scale graph analyzer 100 comprises a logical deductive graphdatabase 110, a semantic reasoner 120, a stream processing engine 130,and an event log 131. The large-scale graph analyzer 100 receives andinput query 140, retrieves and transforms the relevant data, deduces anypertinent new data, and produces an output result 141. A secondaryoutput 142 in the form of an event stream provides users with a dataprovenance log.

According to one aspect, a semantic reasoner 120, software that infers alogical consequence from a set of facts, as in the case of fact tablesin a deductive database, comprises an interference engine 121, anontology language 122, and a logic programming language 123. Theinference engine 121 may work in one of two modes: forward chaining andbackwards chaining. Other embodiments may comprise probabilisticreasoners, non-axiomatic reasoners, or a probabilistic logic network.Embodiments may also employ any number of ontology languages 122,traditional syntax languages (e.g., Common Logic, Frame-logic, KnowledgeInterchange Format, etc.), markup ontology languages (e.g., OntologyInference Layer, Web Ontology Language, Resource Description Framework,etc.), and controlled or open vocabulary natural languages (e.g.,Attempto Controlled English, Executable English, etc.). Embodiments mayalso comprise any logic programming language 123 where the languagerules are written in the forms of clauses and are read declaratively aslogical implications (e.g., Prolog, Answer Set Programming, andDatalog).

The semantic reasoner 120 may further be used for schema generationwhere the deductive capabilities of the semantic reasoner 120 via theinference engine 121 coupled with the logic and ontology languages122/123 provide the means to deduce new information. This isaccomplished by using forwards and backwards chaining techniques of theinference engine which is bounded, informed, and enforced by theontological and logic languages. Integrating deduced information intothe existing fact table (fact tables suitable for use with programminglogic languages for deductive reasoning) ontology in addition toanswering queries allows for the reliable schema generation of sparsedata sets.

According to one aspect, the logical deductive graph database 110comprises a graph database 111, a translation service 112, a shardingservice 113, and at least one fact table 114. Graph databases 111 maycomprise both public and private databases, co-located or geographicallydispersed, and comprised of any storage technology and data model. Thetranslation service 112 comprises a series of transformation modulesthat convert any storage technology and data model to a fact tablemodel. In general, each transformation module converts the graph nodesand relationships to a fact, rule, or constraint or any combinationthereof. The sharding service 113 breaks up large tables into smallerchunks called shards that are spread across multiple servers. Thisallows significantly large fact tables to be generated from theingestion of large amounts of graph data. Other embodiments may useother distributed persistence techniques such as replication,federation, or rely on cloud-based persistence vendors. Fact tables 114may be equally persisted by replicating, partitioning, and federationstrategies while optionally being co-located or cloud-based.

The stream processing engine 130 typically comprises processes thatmanage queries or programs through distributed execution of event-drivenapplication whereby stateful computations may be achieved over streamingpipelines. According to one embodiment, Apache Flink® with a graphprocessing API such as Gelly may be used to coordinate the ingestion ofgraph data 111 to the translation service (ran as an Flink program code)and output to the sharding service 113. Furthermore, the shardingservice 113 by be directed by the stream processing engine 130classifier stages (Flink job) to ascertain the most appropriate node.Chains of classifiers may be arranged hierarchically to optimize dataingestion pertaining to precise portions of the graph data. Other streamprocessing engines 113 may be used such as Spark, Kafka, and Beam. Theintegration of specific software platforms is not limiting to thisembodiment. A person of ordinary skill in the art will be aware of otherpossible combinations of stream processing engines 113, reasoners(otherwise known as inference engines and rule engines) 120/121,ontology languages 122, and logic languages 123 available forimplementation.

One example of the large-scale graph analyzer 110 follows: consider oneDNA sequencing company acquires a competing DNA sequencing company. Thecomplex task of combining both company's databases now arises. Considerfurther, the task is not to simply combine the data and removeduplicates, but the company must now also use both sets of informationto deduce new relations between the persons sequenced. This must happenthrough logical deduction and must also be auditable.

The company must now use the large-scale graph analyzer 100 to ingestboth databases, translate both into table format, use the logic languageto combine data, use the inference engine 121 to deduce new relations,and convert back into graph data. The process of converting fact tableback to graph data is simply the inverse operation where the event log131 may inform the system as to how the data was initially converted.Other embodiments may use other modules or functions known in the art toperform conversions and translations.

In the event data becomes corrupted, a syncing error occurred, orimproper relations are reported by customers, the company may simplychange their programming logic and try again as the original data wasingested into the fact tables 114 and not overwritten. In some cases, orembodiments where auditability is important or core data was lost ordamaged, data provenance 142 techniques may be used to infer, correct,and restore such problems.

FIG. 2 is a block diagram explaining an exemplary method for translatinggraph data into data suitable for deductive reasoning. The left side ofthe diagram represents data 210 found in a graph database. The rightside represents data stored in tables 220 as found in a deductivedatabase. According to this method, nodes 211/212 and relationships 213between nodes 211/212 are copied, moved, or otherwise translated to adeductive database 220 as a set of basic facts 221, a bank of procedures222, and a set of rules for inference 223. Below are example programminglogic statements that nodes and edges must be translated into:

-   -   Basic Fact: F(x, y)    -   Bank of Procedures: F1(x,y)←F2(x,y)        -   F1(x,y)←F3(x,y)    -   Rules for Inference: I(x)←F2(x,y){circumflex over ( )}F3(x,y)

As one example, a genealogy tree of a father named James 214 and a sonnamed Jack 216 exists in a knowledge graph with standard native propertygraph format. Persons, namely James and Jack, are stored as nodes214/216 along with any associated data such as age. The relationshipbetween the father and son is stored as an edge 215. Using a nativeproperty graph query language like Cypher, one may extract the nodes214/216 and relationships 215, and then use the returned data to specifya fact table entry for each result. More specifically, the large-scalegraph analyzer while mapping relationships between parents and a child,deduces that a father of a child is also a parent. Furthermore, shouldthe knowledge graph include Jack's mother, the large-scale graphanalyzer may then deduce that a parent can be either a mother or afather but not both. Procedures and inferences may be manuallyprogrammed, or various automated methods may be used.

-   -   Basic Facts: Person James, 37)        -   Person Jack, “null”)        -   FatherJames, Jack)        -   Son Jack, James)    -   Bank of Procedures: Parent(x,y)←Father(x,y)        -   Child(x,y)←Son(x,y)        -   Parent(x,y)←Mother(x,y)    -   Rules of Inference: IC(x)←Father(x,y){circumflex over        ( )}Mother(x,y)

The method shown in this example is not limiting of the invention asthis is simply an example of one type of translation for purposes ofunderstanding and explanation. Many means may be used to implement thetranslation. For example, manual construct a translation module for eachcombination of data structure and model is possible, but a moreefficient means of implementation would be to use machine learningalgorithms to convert graph data within bounded operating parameters.Persons of ordinary skill in the art may employ different methods andtechniques for graph to relational conversions.

Detailed Description of Exemplary Aspects

FIG. 3 is a flow diagram illustrating an exemplary method for alarge-scale graph analyzer, according to one embodiment. The first stepin deploying the large-scale graph analyzer is to in set up the semanticreasoner 301. This can be done by first choosing one or more ontologylanguages, where an ontology language is a formal construct to allow theencoding of knowledge about specific domains and support the processingof that knowledge through a plurality of reasoning rules. The secondstep to setting up the semantic reasoner 301 is to choose a logicprogramming language which is a programming paradigm based on formallogic.

Lastly, any number of existing inference engines may be used, or one canbe programmed specific for the application desired by the user. Publicand private graph data sources must be set up and the translationmodules for each data storage type and model must be implemented. API'stypically are used to communication between software platforms anddatabases, or other standardized protocols such as HTTP, FTP, etc.

Upon completion of the configuration, a user posits a query 302 throughthe programming logic language, which is typically mediated and boundedby the ontology language chosen. The query is received by the streamprocessing engine where distributed processing and storage handle thelarge amount of graph data needed for the query 303. Not all queriesneed a large amount of data, this example uses the highest degree ofdifficulty for exemplary purposes. The stream processing engine queriesthe graph databases 304 for relevant nodes and edges, whereby the graphdatabases return said relevant nodes and edges 305.

The translation service identifies the type of storage structure anddata model in order to call the appropriate translation module that willconvert the retrieved data into tabular form 306. As graph data isconverted/translated, the sharding service, informed by the streamprocessing engine, allocates distributed shards of data 307 forming theoverall fact table for the query. The stream processing engine upondetecting the graph data retrieval and conversion is complete, notifiesthe semantic reasoner to complete the query 308.

If the query is satisfied 309 by the existing fact table, the resultsare given 310. Should the query not meet the threshold for satisfaction,the inference engine performs either backwards or forward chaining todeduce the information 311 required to satisfy the query, at which pointthe results will then be given 310. During the steps from 303-311, thestream processing engine is logging all events 312 and providing a meansby which data provenance 313 is possible. Some embodiments may usemachine learning algorithms and the data provenance to self-authentictrustworthy results. Other embodiments may use data provenance forauditability function and further embodiments may use the event log fordata redundancy.

FIG. 4 is a flow diagram illustrating an exemplary method for graph dataconversion, according to one aspect. Regarding the translation of graphdata into relation data tables, the following diagram provides anexemplary decision tree for converting various common graph storage andgraph data model types to relational data (facts suitable for use withprogramming logic languages for deductive reasoning).

Upon receiving a query 401 and retrieving a chunk of relevant graph datain return 402, two characteristics of the graph data must be obtained.The type of storage technology 403 (e.g., native graph, object-oriented,relational, key-value, etc.) and the data model 404 (e.g., propertygraph, hyper graph, triple store, etc.). Additionally, and obvious tothe user, the storage and model format for which to convert the graphdata too must be predetermined. Typically, this is already resolved bythe IT staff or engineering department. Any fact table or fact tablesuitable for use with programming logic languages for deductivereasoning may be used as the destination format granted the translationmodules are written for such a database.

Typically, for each data conversion, only one type of graph storagetransformation (405 a-n) and only one type of graph data model existsper database (406 a-n). FIG. 2 was an example of a method for thetransformation of native property graphs 405 a/406 a. The samemethodology may be extrapolated for use in the other technologies andmodels (405 b-n and 406 b-n).

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 5, there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 5 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 6, there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20, and may be useful for providing commonservices to client applications 24. Services 23 may for example beWINDOWS™ services, user-space common services in a Linux environment, orany other type of common service architecture used with operating system21. Input devices 28 may be of any type suitable for receiving userinput, including for example a keyboard, touchscreen, microphone (forexample, for voice input), mouse, touchpad, trackball, or anycombination thereof. Output devices 27 may be of any type suitable forproviding output to one or more users, whether remote or local to system20, and may include for example one or more screens for visual output,speakers, printers, or any combination thereof. Memory 25 may berandom-access memory having any structure and architecture known in theart, for use by processors 21, for example to run software. Storagedevices 26 may be any magnetic, optical, mechanical, memristor, orelectrical storage device for storage of data in digital form (such asthose described above, referring to FIG. 5). Examples of storage devices26 include flash memory, magnetic hard drive, CD-ROM, and/or the like.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 7, there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a system 20 such as that illustrated inFIG. 6. In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as WiFi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises. In addition to local storage on servers 32, remotestorage 38 may be accessible through the network(s) 31.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 in either local or remote storage 38 may be used orreferred to by one or more aspects. It should be understood by onehaving ordinary skill in the art that databases in storage 34 may bearranged in a wide variety of architectures and using a wide variety ofdata access and manipulation means. For example, in various aspects oneor more databases in storage 34 may comprise a fact table system using astructured query language (SQL), while others may comprise analternative data storage technology such as those referred to in the artas “NoSQL” (for example, HADOOP CASSANDRA™, GOOGLE BIGTABLE™, and soforth). In some aspects, variant database architectures such ascolumn-oriented databases, in-memory databases, clustered databases,distributed databases, or even flat file data repositories may be usedaccording to the aspect. It will be appreciated by one having ordinaryskill in the art that any combination of known or future databasetechnologies may be used as appropriate, unless a specific databasetechnology or a specific arrangement of components is specified for aparticular aspect described herein. Moreover, it should be appreciatedthat the term “database” as used herein may refer to a physical databasemachine, a cluster of machines acting as a single database system, or alogical database within an overall database management system. Unless aspecific meaning is specified for a given use of the term “database”, itshould be construed to mean any of these senses of the word, all ofwhich are understood as a plain meaning of the term “database” by thosehaving ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 8 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to peripherals such as a keyboard49, pointing device 50, hard disk 52, real-time clock 51, a camera 57,and other peripheral devices. NIC 53 connects to network 54, which maybe the Internet or a local network, which local network may or may nothave connections to the Internet. The system may be connected to othercomputing devices through the network via a router 55, wireless localarea network 56, or any other network connection. Also shown as part ofsystem 40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with thesystem of any particular aspect, and such modules may be variouslyimplemented to run on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for analyzing graph databases usingintelligent reasoning systems including scalable collection of, andtransformation of, graph data into facts for use with programming logiclanguages for deductive reasoning, comprising: a computing devicecomprising a memory, a processor, and a non-volatile data storagedevice; a stream processing engine comprising a first plurality ofprogramming instructions stored in the memory of, and operating on theprocessor of, the computing device, wherein the first plurality ofprogramming instructions, when operating on the processor, cause thecomputing device to: receive an ontology-mediated query, wherein theontology-mediated query is constructed by a programming logic language;ingest one or more graph-based databases related to theontology-mediated query; identify a storage technology and data model ofeach graph-based database; and send the identified storage technologyand data model of each graph-based database to a translation service; atranslation service comprising a second plurality of programminginstructions stored in the memory of, and operating on the processor of,the computing device, wherein the second plurality of programminginstructions, when operating on the processor, cause the computingdevice to: convert each graph-based database to facts suitable for usewith programming logic languages for deductive reasoning, wherein theconversion is based on the identified storage technology and data model;and send the facts suitable for use with programming logic languages fordeductive reasoning to a sharding service; a sharding service comprisinga second plurality of programming instructions stored in the memory of,and operating on the processor of, the computing device, wherein thesecond plurality of programming instructions, when operating on theprocessor, cause the computing device to: combine the facts suitable foruse with programming logic languages for deductive reasoning from thegraph-based databases into a fact table; and a semantic reasonercomprising a third plurality of programming instructions stored in thememory of, and operating on the processor of, the computing device,wherein the third plurality of programming instructions, when operatingon the processor, cause the computing device to: satisfy theontology-mediated query by analyzing the fact table; and output theontology-mediated query results.
 2. The system of claim 1, furthercomprising an event log, wherein the event log provides a means for dataprovenance.
 3. The system of claim 1, wherein the stream processingengine uses distributed computing to process the ontology-mediatedquery.
 4. The system of claim 1, further comprising a semantic reasoner.5. The system of claim 4, wherein the semantic reasoner deduces newinformation from the fact table.
 6. The system of claim 5, wherein thenew information is integrated into the fact table.
 7. The system ofclaim 6, wherein the semantic reasoner uses the fact table to satisfy ahypothetical query.
 8. A method analyzing graph databases usingintelligent reasoning systems including scalable collection of, andtransformation of, graph data into facts for use with programming logiclanguages for deductive reasoning, comprising the steps of: receiving anontology-mediated query, wherein the ontology-mediated query isconstructed by a programming logic language; ingesting one or moregraph-based databases related to the ontology-mediated query;identifying a storage technology and data model of each graph-baseddatabase; converting each graph-based database to facts suitable for usewith programming logic languages for deductive reasoning, wherein theconversion is based on the identified storage technology and data model;combining the facts suitable for use with programming logic languagesfor deductive reasoning from the graph-based databases into a facttable; and providing the fact table to the programming logic language tosatisfy the ontology-mediated query. satisfying the ontology-mediatedquery by analyzing the fact table; and outputting the ontology-mediatedquery results.
 9. The method of claim 8, further comprising an eventlog, wherein the event log provides a means for data provenance.
 10. Themethod of claim 8, wherein the stream processing engine uses distributedcomputing to process the ontology-mediated query.
 11. The method ofclaim 8, further comprising a semantic reasoner.
 12. The method of claim11, wherein the semantic reasoner deduces new information from the facttable.
 13. The method of claim 12, wherein the new information isintegrated into the fact table.
 14. The method of claim 13, wherein thesemantic reasoner uses the fact table to satisfy a hypothetical query.